If you want to try out the voice cloning yourself you can do that an this Hugging Face demo: https://huggingface.co/spaces/Qwen/Qwen3-TTS - switch to the "Voice Clone" tab, paste in some example text and use the microphone option to record yourself reading that text - then paste in other text and have it generate a version of that read using your voice.
This is terrifying. With this and z-image-turbo, we've crossed a chasm. And a very deep one. We are currently protected by screens, we can, and should assume everything behind a screen is fake unless rigorously (and systematically, i.e. cryptographically) proven otherwise. We're sleepwalking into this, not enough people know about it.
That was my thought too. You’d have “loved ones” calling with their faces and voices asking for money in some emergency. But you’d also have plausible deniability as anything digital can be brushed off as “that’s not evidence, it could be AI generated”.
Far more terrifying is Big Tech having access to a closed version of the same models, in the hands of powerful people with a history of unethical behavior (i.e. Zuckerberg's "Dumb Fucks" comments). In fact it's a miracle and a bit ironic that the Chinese would be the ones to release a plethora of capable open source models, instead of the scraps like we've seen from Google, Meta, OpenAI, etc.
I'd be a bit more worried with Z-Image Edit/Base is release.
Flux.2 Klein is our and its on par with Zit, and with some fine tuning can just about hit Flux.2.
Adding on top of that is Qwen Image Edit 2511 for additional refinement. Anything is possible. Those folks at r/StableDiffusion and falling over the possible release of Z-Image-Omni-Base, a hold me over until actual base is out. I've heard its equal to Flux.2.
Crazy time.
> With this and z-image-turbo, we've crossed a chasm.
And most of all: they're both local models. The cat is out of the box and it's never going back in. There's no censoring of this. No company that can pull the plug. Anyone with a semi-modern GPU can use these models.
Admittedly I have not dove into it much but, I wonder if we might finally have a usecase for NFTs and web3? We need some sort of way to denote items are persion generated not AI. Would certainly be easier than trying to determine if something is AI generated
There are far more good and interesting use cases for this technology. Games will let users clone their voices and create virtual avatars and heroes. People will have access to creative tools that let them make movies and shows with their likeness. People that couldn't sing will make music.
Nothing was more scary than the invention of the nuclear weapon. And we're all still here.
Life will go on. And there will be incredible benefits that come out of this.
The HF demo space was overloaded, but I got the demo working locally easily enough. The voice cloning of the 1.7B model captures the tone of the speaker very well, but I found it failed at reproducing the variation in intonation, so it sounds like a monotonous reading of a boring text.
I presume this is due to using the base model, and not the one tuned for more expressiveness.
edit: Or more likely, the demo not exposing the expressiveness controls.
The 1.7B model was much better at ignoring slight background noise in the reference audio compared to the 0.6B model though. The 0.6B would inject some of that into the generated audio, whereas the 1.7B model would not.
Also, without FlashAttention it was dog slow on my 5090, running at 0.3X realtime with just 30% GPU usage. Though I guess that's to be expected. No significant difference in generation speed between the two models.
Overall though, I'm quite impressed. I haven't checked out all the recent TTS models, but a fair number, and this one is certainly one of the better ones in terms of voice cloning quality I've heard.
Remarkable tech that is now accessible to almost anyone. My cloned voice sounded exactly like me. The uses for this will be from good to bad and everywhere in-between. A deceased grandmother reading "Good Night Moon" to grandkids, scamming people, the ability to create podcasts with your own voices from just prompts.
I got some errors trying to run this on my MBP. Claude was able to one-shot a fix.
```
Loaded speech tokenizer from ~/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-1.7B-VoiceDesign/snapshots/0e711a1c0aa5aad30654426
e0d11f67716c1211e/speech_tokenizer
Fetching 11 files: 0%| | 0/11 [00:00<?, ?it/s]Fetching 11 files: 100%|| 11/11 [00:00<00:00, 125033.45it/s]
The tokenizer you are loading from
'!/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-1.7B-VoiceDesign/snapshots/0e711a1c0aa5aad30654426e0d11f67716c1211e' with an
incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instr.... This will
lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue.
```
I cloned my voice and had it generate audio for a paragraph from something I wrote. It definitely kind of sounds like me, but I like it much better than listening to my real voice. Some kind of uncanny peak.
You do realize that you don't hear your real voice normally, an individual has to record their voice to hear how others hear their voice. What you hear when you speak includes your skull resonating, which other's do not hear.
If i am ever in the same city as you, i'll buy you dinner. I poked around during my free time today trying to figure out how to run these models, and here is the estimable Simon Willison just presenting it on a platter.
hopefully i can make this work on windows (or linux, i guess).
the old voice cloning and/or TTS models were CPU only, and they weren't realtime, but no worse than 2:1, 30 seconds of audio would take 60 seconds to generate. roughly. in 2021 one-shot TTS/cloning using GPUs was getting there, and that was close enough to realtime; one could, if one was willing to deal with it, wire microphone audio to the model, and speak words, and the model would, in real time, modify the voice. Phil Hendrie is jealous.
anyhow, with faster CPUs and optimizations, you won't be waiting too long. Also 20GB is overkill for an audio model. Only text - LLM - are huge and take infinite memory. SD/FLUX models are under 16GB of ram usage (uh, mine are, at least!), for instance.
Interesting model, I've managed to get the 0.6B param model running on my old 1080 and I can generated 200 character chunks safely without going OOM, so I thought that making an audiobook of the Tao Te Ching would be a good test. Unfortunately each snippet varies drastically in quality: sometimes the speaker is clear and coherent, but other times it bursts out laughing or moaning. In a way it feels a bit like magical roulette, never being quite certain of what you're going to get. It does have a bit of charm, when you chain the various snippets together you really don't know what direction it's gonna go.
Using speaker Ryan seems to be the most consistent, I tried speaker Eric and it sounded like someone putting on a fake exaggerated Chinese accent to mock speakers.
If it wasn't for the unpredictable level of emotions from each chunk, I'd say this is easily the highest quality TTS model I've tried.
Have you tried specifying the emotion? There's an option to do so and if it's left empty it wouldn't surprise me if it defaulted to rng instead of bland.
Yeah, it's not great. I wrote a harness that calculates it as: 3.61s Load Time, 38.78s Gen Time, 18.38s Audio Len, RTF 2.111.
The Tao Te Ching audiobook came in at 62 mins in length and it ran for 102 mins, which gives an RTF of 1.645.
I do get a warning about flash-attn not being installed, which says that it'll slow down inference. I'm not sure if that feature can be supported on the 1080 and I wasn't up for tinkering to try.
it isn't often that tehcnology gives me chills, but this did it. I've used "AI" TTS tools since 2018 or so, and i thought the stuff from two years ago was about the best we were going to get. I don't know the size of these, i scrolled to the samples. I am going to get the models set up somewhere and test them out.
Now, maybe the results were cherrypicked. i know everyone else who has released one of these cherrypicks which to publish. However, this is the first time i've considered it plausible to use AI TTS to remaster old radioplays and the like, where a section of audio is unintelligible but can be deduced from context, like a tape glitch where someone says "HEY [...]LAR!" and it's an episode of Yours Truly, Johnny Dollar...
I have dozens of hours of audio of like Bob Bailey and people of that era.
scroll down to the second to last group, the second one down is obama speaking english, the third one down is trump speaking japanese (a translation of the english phrase)
besides, they know what side their bread is buttered on. I feel like this is almost not the real announcement; or, the engineers that wrote this up and did the demos just ran it that way. The normal speech voices are fine (lower than the anime ones on the page.) i agree that the first few are very infantile. I'll change that word if i can think of a better one.
Indeed, I have a future project/goal of "restoring" Have Gun - Will Travel radio episodes to listenable quality using tech like this. There are so many lines where sound effects and tape rot and other "bad recording" things make it very difficult to understand what was sad. Will be amazing, but as with all tech the potential for abuse is very real
hey if you want to collab or trade notes, my email is in my profile.
there was java software that did FANTASTIC work cleaning up crappy transfers of audio, like, specifically, it was perfect for "AM Quality Monaural Audio".
Observe, original: https://www.youtube.com/watch?v=YiRcOVDAryM
my edit (took about an hour, if memory serves, to set up. forgot render time...): https://www.youtube.com/watch?v=xazubVJ0jz4
i say "was [...] software" because the last 2 times i've tried to use it, it did imperceptible cleanup, making it worthless. Anyhow, all my radio plays are from OTRR, i think.
Audio.Restoration.DeNoise.DeNoiseLF.2.8.3_WiN.OSX is a more recent version i think
Have you tested alternatives? I grabbed Open Code and a Minimax m2.1 subscription, even just the 10usd/mo one to test with.
Result? We designed a spec for a slight variation of a tool for which I wrote a spec with Claude - same problem (process supervisor tool), from scratch.
Honestly, it worked great, I have played a little further with generating code (this time golang), again, I am happy.
I've been using GLM 4.7 with Claude Code. best of both worlds. Canceled my Anthropic subscription due to the US politics as well. Already started my "withdrawal" in Jan 2025, Anthropic was one of the few that was left
With a good harness I am getting similar results with GLM 4.7. I am paying for TWO! max accounts and my agents are running 24/7.
I still have a small Claude account to do some code reviews. Opus 4.5 does good reviews but at this point GLM 4.7 usually can do the same code reviews.
If cost is an issue (for me it is, I pay out of pocket) go with GLM 4.7
Your GitHub profile is... disturbing. 1,354 commits and 464 pull requests in January so far.
Regardless of how productive those numbers may seem, that amount of code being published so quickly is concerning, to say the least. It couldn't have possibly been reviewed by a human or properly tested.
If this is the future of software development, society is cooked.
I've been using GLM 4.7 alongside Opus 4.5 and I can't believe how bad it is. Seriously.
I spent 20 minutes yesterday trying to get GLM 4.7 to understand that a simple modal on a web page (vanilla JS and HTML!) wasn't displaying when a certain button was clicked. I hooked it up to Chrome MCP in Open Code as well.
It constantly told me that it fixed the problem. In frustration, I opened Claude Code and just typed "Why won't the button with ID 'edit' work???!"
It fixed the problem in one shot. This isn't even a hard problem (and I could have just fixed it myself but I guess sunk cost fallacy).
I use Opus 4.5 for planning, when I reach my usage limits fallback to GLM 4.7 only for implementing the plan, it still struggles, even though I configure GLM 4.7 as both smaller model and heavier model in claude code
The Chinese labs distill the SOTA models to boost the performance of theirs. They are a trailer hooked up (with a 3-6 month long chain) to the trucks pushing the technology forwards. I've yet to see a trailer overtake it's truck.
China would need an architectural breakthrough to leap American labs given the huge compute disparity.
Care to explain how the volume of AI research papers authored by Chinese researchers[1] has exceeded US-published ones? Time-traveling plagiarism perhaps, since you believe the US is destined to lead always.
1. Chinese researcher in China, to be more specific.
I could say the same about grok (although given there are better models for my use cases I don't use it). What part of divisive politics are you talking about here?
In my tests this doesn't come close to the years old coqui/XTTS-v2. It has great voice cloning capabilities and creates rich speech with emotions with low latency. I tried out several local-TTS projects over the years but i'm somewhat confused that nothing seems to be able to match coqui despite the leaps that we see in other areas of AI. Can somebody with more knowledge in this field explain why that might be? Or am i completely missing something?
Amusingly one of their examples (the final Age Control example) is prompted to have American English as an accent, but sounds like an Australian trying to sounds American to my ear haha
Has anyone successfully run this on a Mac? The installation instructions appear to assume an NVIDIA GPU (CUDA, FlashAttention), and I’m not sure whether it works with PyTorch’s Metal/MPS backend.
FWIW you can run the demo without FlashAttention using --no-flash-attn command-line parameter, I do that since I'm on Windows and haven't gotten FlashAttention2 to work.
Thanks! Simon's example uses the custom voice model (creating a voice from instructions). But that comment led me eventually to this page, which shows how to use mlx-audio for custom voices:
Readit News
I shared a recording of audio I generated with that here: https://simonwillison.net/2026/Jan/22/qwen3-tts/
Far more terrifying is Big Tech having access to a closed version of the same models, in the hands of powerful people with a history of unethical behavior (i.e. Zuckerberg's "Dumb Fucks" comments). In fact it's a miracle and a bit ironic that the Chinese would be the ones to release a plethora of capable open source models, instead of the scraps like we've seen from Google, Meta, OpenAI, etc.
And most of all: they're both local models. The cat is out of the box and it's never going back in. There's no censoring of this. No company that can pull the plug. Anyone with a semi-modern GPU can use these models.
There are far more good and interesting use cases for this technology. Games will let users clone their voices and create virtual avatars and heroes. People will have access to creative tools that let them make movies and shows with their likeness. People that couldn't sing will make music.
Nothing was more scary than the invention of the nuclear weapon. And we're all still here.
Life will go on. And there will be incredible benefits that come out of this.
I presume this is due to using the base model, and not the one tuned for more expressiveness.
edit: Or more likely, the demo not exposing the expressiveness controls.
The 1.7B model was much better at ignoring slight background noise in the reference audio compared to the 0.6B model though. The 0.6B would inject some of that into the generated audio, whereas the 1.7B model would not.
Also, without FlashAttention it was dog slow on my 5090, running at 0.3X realtime with just 30% GPU usage. Though I guess that's to be expected. No significant difference in generation speed between the two models.
Overall though, I'm quite impressed. I haven't checked out all the recent TTS models, but a fair number, and this one is certainly one of the better ones in terms of voice cloning quality I've heard.
``` Loaded speech tokenizer from ~/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-1.7B-VoiceDesign/snapshots/0e711a1c0aa5aad30654426 e0d11f67716c1211e/speech_tokenizer Fetching 11 files: 0%| | 0/11 [00:00<?, ?it/s]Fetching 11 files: 100%|| 11/11 [00:00<00:00, 125033.45it/s] The tokenizer you are loading from '!/.cache/huggingface/hub/models--Qwen--Qwen3-TTS-12Hz-1.7B-VoiceDesign/snapshots/0e711a1c0aa5aad30654426e0d11f67716c1211e' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instr.... This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue. ```
What am I doing wrong?
That's not really rational considering the internet is full of examples of my voice that anyone could use though. Here's a recent podcast clip: https://www.youtube.com/watch?v=lVDhQMiAbR8&t=3006s
Here's the script I'm using: https://github.com/simonw/tools/blob/main/python/q3_tts.py
You can try it with uv (downloads a 4.5GB model on first run) like this:
hopefully i can make this work on windows (or linux, i guess).
thanks so much.
mlx-audio only works on Apple Silicon
You'd need to use a different build of the model though, I don't think MLX has a CPU implementation.
anyhow, with faster CPUs and optimizations, you won't be waiting too long. Also 20GB is overkill for an audio model. Only text - LLM - are huge and take infinite memory. SD/FLUX models are under 16GB of ram usage (uh, mine are, at least!), for instance.
Using speaker Ryan seems to be the most consistent, I tried speaker Eric and it sounded like someone putting on a fake exaggerated Chinese accent to mock speakers.
If it wasn't for the unpredictable level of emotions from each chunk, I'd say this is easily the highest quality TTS model I've tried.
> Read this in a calm, clear, and wise audiobook tone.
> Do not rush. Allow the meaning to sink in.
But maybe I should experiment with something more detailed. Do you have any suggestions?
The Tao Te Ching audiobook came in at 62 mins in length and it ran for 102 mins, which gives an RTF of 1.645.
I do get a warning about flash-attn not being installed, which says that it'll slow down inference. I'm not sure if that feature can be supported on the 1080 and I wasn't up for tinkering to try.
Now, maybe the results were cherrypicked. i know everyone else who has released one of these cherrypicks which to publish. However, this is the first time i've considered it plausible to use AI TTS to remaster old radioplays and the like, where a section of audio is unintelligible but can be deduced from context, like a tape glitch where someone says "HEY [...]LAR!" and it's an episode of Yours Truly, Johnny Dollar...
I have dozens of hours of audio of like Bob Bailey and people of that era.
besides, they know what side their bread is buttered on. I feel like this is almost not the real announcement; or, the engineers that wrote this up and did the demos just ran it that way. The normal speech voices are fine (lower than the anime ones on the page.) i agree that the first few are very infantile. I'll change that word if i can think of a better one.
Audio.Restoration.DeNoise.DeNoiseLF.2.8.3_WiN.OSX is a more recent version i think
p.s. are you a "dude named Ben"?
Deleted Comment
Although I like the model, I don't like the leadership of that company and how close it is, how divisive they're in terms of politics.
Have you tested alternatives? I grabbed Open Code and a Minimax m2.1 subscription, even just the 10usd/mo one to test with.
Result? We designed a spec for a slight variation of a tool for which I wrote a spec with Claude - same problem (process supervisor tool), from scratch.
Honestly, it worked great, I have played a little further with generating code (this time golang), again, I am happy.
Beyond that, Glm4.7 should also be great.
See https://dev.to/kilocode/open-weight-models-are-getting-serio...
It is a recent case story of vibing a smaller tool with kilo code, comparing output from minimax m2.1 and Glm4.7
Honestly, just give it a whirl - no need to send money to companies/nations your disagree with with.
What do you mean by this?
https://www.bloomberg.com/news/articles/2026-01-20/anthropic...
I still have a small Claude account to do some code reviews. Opus 4.5 does good reviews but at this point GLM 4.7 usually can do the same code reviews.
If cost is an issue (for me it is, I pay out of pocket) go with GLM 4.7
Regardless of how productive those numbers may seem, that amount of code being published so quickly is concerning, to say the least. It couldn't have possibly been reviewed by a human or properly tested.
If this is the future of software development, society is cooked.
I spent 20 minutes yesterday trying to get GLM 4.7 to understand that a simple modal on a web page (vanilla JS and HTML!) wasn't displaying when a certain button was clicked. I hooked it up to Chrome MCP in Open Code as well.
It constantly told me that it fixed the problem. In frustration, I opened Claude Code and just typed "Why won't the button with ID 'edit' work???!"
It fixed the problem in one shot. This isn't even a hard problem (and I could have just fixed it myself but I guess sunk cost fallacy).
I use Opus 4.5 for planning, when I reach my usage limits fallback to GLM 4.7 only for implementing the plan, it still struggles, even though I configure GLM 4.7 as both smaller model and heavier model in claude code
China would need an architectural breakthrough to leap American labs given the huge compute disparity.
1. Chinese researcher in China, to be more specific.
because i've been on youtube and insta, and believe me, no one else even compares, yet.
https://huggingface.co/mlx-community/Qwen3-TTS-12Hz-0.6B-Bas...